Inferring maps of forces inside cell membrane microdomains

TL;DR

This paper introduces advanced inference methods to accurately map forces and diffusion coefficients within cell membrane microdomains, enhancing the understanding of membrane compartmentation through analysis of biomolecule trajectories.

Contribution

It presents a novel inference framework that fully utilizes trajectory data to estimate forces and potentials in membrane microdomains, outperforming traditional methods.

Findings

Reliable inference demonstrated on simulated trajectories

Applied to receptor labeled by lanthanide-ion nanoparticles

Results highlight the method's general applicability to membrane studies

Abstract

Mapping of the forces on biomolecules in cell membranes has spurred the development of effective labels, e.g. organic fluorophores and nanoparticles, to track trajectories of single biomolecules. Standard methods use particular statistics, namely the mean square displacement, to analyze the underlying dynamics. Here, we introduce general inference methods to fully exploit information in the experimental trajectories, providing sharp estimates of the forces and the diffusion coefficients in membrane microdomains. Rapid and reliable convergence of the inference scheme is demonstrated on trajectories generated numerically. The method is then applied to infer forces and potentials acting on the receptor of the $\epsilon$-toxin labeled by lanthanide-ion nanoparticles. Our scheme is applicable to any labeled biomolecule and results show show its general relevance for membrane compartmentation.